Increasingly, large networks of surveillance cameras are employed to monitor public and private facilities. This continuous collection of imagery has the potential for tremendous impact on public safety and security. Unfortunately, this potential is often unrealized since manual monitoring of growing numbers of video feeds is not feasible. As a consequence, surveillance video is mostly stored without being viewed and is only used for data-mining and forensic needs. However, the ability to perform computer-based video analytics is now becoming possible, enabling a proactive approach where security personnel can be continually appraised of who is on site, where they are, and what they are doing. Under this new paradigm, a significantly higher level of security can be achieved through the increased productivity of security officers. The ultimate goal of intelligent video for security and surveillance is to automatically detect events and situations that require the attention of security personnel. Augmenting security staff with automatic processing will increase their efficiency and effectiveness. This is a difficult problem since events of interest are complicated and diverse. (Tu et al., 2007; Krahnstoever et al., 2009) discuss some of the challenges of developing surveillance systems, and present an overview of some solutions concerning with people detection, crowd analysis, multi-camera multi-target tracking, event detection, indexing, and search.
Dynamic Background Modeling
In order to extract the desired higher-level information, as an intermediate step, several video analysis tasks rely on modeling the background in order to detect the presence of foreground objects of interest. While several methods are available for simple scenarios, the case of a moving camera, observing objects moving in a scene with severe motion clutter, is still considered a challenge. (Kim et al., 2009) addresses this issue by providing a model for the background that takes into account the camera motion, as well as the motion clutter. Detecting a foreground object is equivalent to detecting a model change. This is done optimally online by exploiting the sequential generalized likelihood ratio test, applied to the sufficient test statistic that describes the motion clutter.
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